Laura Gagliano, Elie Bou Assi, Dang K. Nguyen et Mohamad Sawan
Article de revue (2019)
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Abstract
This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. Two features were first extracted from bilateral intracranial electroencephalography (iEEG) recordings of dogs with naturally occurring focal epilepsy. Single-layer LSTM networks were trained to classify 5-min long feature vectors as preictal or interictal. Classification performances were compared to previous work involving multilayer perceptron networks and higher-order spectral (HOS) features on the same dataset. The proposed LSTM network proved superior to the multilayer perceptron network and achieved an average classification accuracy of 86.29% on held-out data. Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction.
Mots clés
biomedical engineering, epilepsy
Sujet(s): |
1900 Génie biomédical > 1900 Génie biomédical 1900 Génie biomédical > 1901 Technologie biomédicale |
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Département: |
Département de génie électrique Institut de génie biomédical |
Centre de recherche: | Autre |
Organismes subventionnaires: | NSERC / CRSNG, Epilepsy Canada, Institute for Data Valorization (IVADO) |
URL de PolyPublie: | https://publications.polymtl.ca/4876/ |
Titre de la revue: | Scientific Reports (vol. 9, no 1) |
Maison d'édition: | Nature |
DOI: | 10.1038/s41598-019-52152-2 |
URL officielle: | https://doi.org/10.1038/s41598-019-52152-2 |
Date du dépôt: | 14 juil. 2021 11:19 |
Dernière modification: | 30 sept. 2023 03:27 |
Citer en APA 7: | Gagliano, L., Bou Assi, E., Nguyen, D. K., & Sawan, M. (2019). Bispectrum and recurrent neural networks: Improved classification of interictal and preictal states. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-52152-2 |
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